Explain PCA Dimensionality Reduction and L2-Normalization Importance
Overview
This question evaluates a data scientist's understanding of core Machine Learning concepts including PCA for dimensionality reduction, the impact of L2-normalization on preprocessing, analytic gradient derivation for logistic regression, classification thresholding and its trade-offs, baseline model selection, and knowledge-informed machine learning. It is commonly asked in technical interviews because it probes mathematical foundations and statistical reasoning alongside model-design judgment, testing a blend of conceptual understanding and practical application within the Machine Learning domain.
Machine-Learning Deep-Dive (Data Scientist Technical Screen)
Scenario
You are discussing core ML concepts and design choices expected in a technical interview setting. Provide concise, principled answers to the following.
Questions
- Principal Component Analysis (PCA)
- Explain how PCA achieves dimensionality reduction.
- Explain why L2-normalization beforehand can matter (clarify what kind of normalization and when).
- Logistic Regression
- Derive the gradient of the binary logistic regression loss via back-propagation/chain rule.
- Classification Thresholding
- When would you move the classification threshold to improve FPR or TPR? Describe the trade-offs and how you’d select a new threshold.
- Baselines and Model Choice
- What baseline models would you compare against, and why might you finally choose logistic regression?
- Knowledge-Informed ML
- Define knowledge-informed (or knowledge-guided) machine learning and give a concrete example.
Solution
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